I have a dataset with three factors that each have two levels. I would like to run the full model:

~ factorA*factorB*factorC

and use the Wald test for significance, but I am having trouble interpreting the results.

I understand from the manual section 3.3 that for a standard model matrix (and since my factors only have two levels, I believe this is correct), if I had two factors factorA and factorB, I would use the following code to extract results:

results(dds, contrast=c("factorA","level1","level2"))

to get the effect of factor A for the reference level of factorB.

My first question is, if I have three factors, would the same code give me the effect of factorA for the reference levels of both factorB and factorC?

If this is true, how do I get the effect of factorA on the other combinations of levels of factorB and factorC?

I understand that to get the effect of factor A in the other level of factorB for two factors, I should use the following:

Ok great, thanks for the help! That makes sense for getting the three-way interaction effects, but if my model is simply ~group, how do I get the main effects for each factor? Would it be legitimate to do:

My suggestion (the link) is for comparing distinct combinations of A, B and C. You would just use a design of ~group and then pick pairs which you want to compare with 'contrast'.

There are many other comparisons to be made with ~A*B*C. If you use this design, you should use: DESeq(dds, betaPrior=FALSE). (this will happen automatically in the October release: designs with interaction terms will turn off the beta prior). You can extract any number of contrasts afterward, see the help for ?results. For the interpretation of each kind of contrast, I would consult a local statistician at your institute. This is more of an interpretation question than a software question, and there will be nothing special about the DESeq2 coefficients (they will be the same as produced by a normal linear regression model).

Thanks for the help, I don't think my question was clear enough. If I run a normal linear regression model ~A*B*C, the output i get is a table with the main effect of each factor, as well as the interactions (2 and 3-way). I am having a hard time getting a similar output from DESeq, specifically, according to the manual, for standard model matrices, I can only get the effect of each condition in the reference levels in the other sets. For the model ~A*B*C, how do I get the main effects of each factor and pairwise interactions from DESeq? Does that make sense?

You can use DESeq2 to test individual terms or contrasts of terms with a Wald test, or you can perform likelihood ratio tests comparing nested models. If you are referring to the anova table for a regular linear model, you would have to perform multiple likelihood ratio tests comparing models with increasing number of terms. We do not have a single function like anova that will order the models and make an Analysis of Deviance table. Note that you can provide matrices to 'full' and 'reduced' as well, if that is easier than providing formula.